Chapter 4
Precision Medicine
What is Precision Medicine?
Precision medicine describes the process of getting the right drug to the right patient at the right time, thereby improving health outcomes. Sometimes called “individualized” or “personalized” medicine, precision medicine most often refers to the use of genetic technologies to determine whether an individual patient is likely to respond to a particular therapy. Today, most approved drugs are effective in only a subset of the patients who take them. Proponents of precision medicine envision a future in which drug therapy is tailored to each patient, leading to better results and lower costs, as wasteful, inappropriate therapy is reduced.
Precision medicine, in the form of molecularly targeted treatments, is already a reality in several types of cancer. But the concept is evolving to include any tools or data that help better tailor medicines and enhance outcomes. In this broader sense, precision medicine is being driven by, and indeed is part of, the many interrelated forces changing the healthcare landscape, including digital technologies, payers' quest for value, and consumer empowerment. Numerous hurdles may delay the widespread adoption of precision medicine. Yet it is already transforming biopharma R & D, commercialization strategies, and business models. Biopharma firms must help address the challenges facing precision medicine by adopting new mind-sets, embracing new digital technologies, and engaging in new kinds of collaborations, both with traditional healthcare stakeholders and new players (Figure 4-1). In doing so, they will ensure that they drive, and are not being driven by, the shift toward more personalized care.
Figure 4-1 Precision medicine in context
Targeted Medicines Multiply But Drug-Diagnostic Pairs are Rare
The rapid increase in the number of targeted cancer drugs, designed for patients with particular genetic mutations, illustrates the growth in precision medicine. Therapies such as Genentech's breast cancer drug Herceptin (trastuzumab), approved for women with high levels of a protein called human epidermal growth factor receptor 2 (HER2), or Novartis's Gleevec (imatinib), approved for chronic myeloid leukemia and gastrointestinal stromal tumors, are used with diagnostic tests that uncover patients' genomic characteristics and thus determine suitability for treatment. These tests are known as companion diagnostics.
In 2006, only five such drug-diagnostic pairs existed, all in oncology, according to data from the Personalized Medicine Coalition. By 2014, as advances in genomics and gene sequencing technologies accelerated, there were well over 100 across several therapy areas, including immunology, cardiovascular disease, gastroenterology, and infectious disease [1]. Among the recent additions: AstraZeneca's non-small cell lung cancer drug Tagrisso (osimertinib) for patients with a very specific mutation that confers resistance to an older kind of targeted therapy, and Vertex's cystic fibrosis treatments Kalydeco (ivacaftor) and Orkambi (ivacaftor/lumacaftor).
More targeted medicines and wider use of biomarkers—measurable substances that can indicate disease incidence and/or whether a drug is having the intended effect—help reduce development time and risk for biopharmaceutical firms by identifying in advance which patients are most likely to respond. A recent study found that drugs developed with a predictive biomarker were three times more likely to be approved than those without [2]. That offers biopharma firms a huge opportunity to increase their R & D efficiency.
However, approved drug-diagnostic combinations remain the exception rather than the rule. Notwithstanding their increased likelihood of regulatory approval, a series of scientific, regulatory, and commercial challenges have hampered the development and widespread adoption of drug-diagnostic pairs. Many of those challenges are being addressed, though this will take time. New genomic sequencing tools and techniques, and the discovery and validation of new and more powerful biomarkers, should support the growth of drug-diagnostic pairs. Important policy initiatives will also accelerate progress, including the United States Precision Medicine Initiative, which promotes research linking genetic information to treatments [3]. This has already spawned a host of data collection initiatives; similar programs are ongoing in other countries.
Precision Medicine is Happening at Several Levels
Precision medicine is not limited to drug-diagnostic combinations. The concept encompasses a wider range of mechanisms and tools used to narrow treatments more precisely to the individual needs of patients and thereby achieve better outcomes. Within the molecular sphere, precision medicine includes various “degrees of targeting”: the most targeted are autologous gene and cell therapies that extract and modify a patient's own cells. These treatments represent the ultimate in individualized medicine: unique therapies that must be customized on a patient-by-patient basis. Drug-diagnostic combinations based on particular sets of genetic mutations or aberrant proteins are less targeted than individualized therapies but more so than drugs targeting processes common to many patients, such as angiogenesis inhibitors, which block the growth of tumor blood vessels (Figure 4-2). These molecularly targeted therapies are in turn far more precise than chemotherapy, which acts on both normal and cancerous cells.
Figure 4-2 Levels of molecular precision medicine
Precision medicine is also set to expand beyond the genome, as scientists attempt to unravel the many other biological steps underlying different pathologies. These include how genes are transcribed (the transcriptome), which proteins they express (the epigenome and the proteome), and which metabolites are produced (the metabolome).
Multiple Forces, Beyond Science, are Driving Precision Medicine
This shift toward more tailored therapies is not being driven only by science. It is being driven by payers, providers, and by patients themselves. It is also being enabled by new digital technologies and data sources, including wearable sensors that enable the real-time capture of physiological and other data, sometimes referred to as “digital biomarkers.” These additional data can help optimize treatment regimens and medication adherence around the lifestyles and priorities of individuals.
Indeed, some groups are starting to use a new term, P-medicine, to describe these broader, yet tightly interlinked, forces driving change across the healthcare landscape, including patient empowerment and digital health. P-medicine envelops personalized, precision, preventative, predictive, pharmacotherapeutic, and patient participatory medicine [4]. Even if many more gene mutations and biomarkers are uncovered, they cannot on their own enable more effective medicine and better outcomes across the board. Only a tiny minority of diseases are understood to be directly linked to particular gene mutations; most result from a far more complex mix of physiological, pathobiological, behavioral, psychological, and environmental factors.
P-medicine encompasses not just the potential of new technologies and science but also a new mindset: toward a greater emphasis on prevention, greater stakeholder collaboration in preempting and solving health challenges, and a greater appreciation for when “no treatment” could represent the best care for a patient. “Precision medicine is technology-inclusive and technology-agnostic in the pursuit of the right therapy for each individual patient,” concludes Mara Aspinall, who was CEO of the diagnostics company Ventana Medical Systems (now part of Roche) and is now executive chairman of GenePeeks and CA Therapeutics and cofounder of the School of Biomedical Diagnostics at Arizona State University. “It is not limited to the patient's genomic profile, or any other single data profile. It's about getting the data necessary and sufficient to match the patient to therapies that will work for them,” she says [5].
This scientific and cultural shift toward more widespread precision medicine—and, ultimately, P-medicine—has profound implications for biopharma R & D and commercial strategies. It is changing the kinds of medicines they develop, how they develop them, and how those medicines are reimbursed and marketed. Drugs are increasingly specific, often biomarker-linked; trials of such treatments require new designs and may benefit from new tools that facilitate patient recruitment and enable patient-reported outcome measures.
Reimbursement, already shifting slowly from fee-for-service to value-based payment models linked to outcomes, will continue to do so, forcing new kinds of biopharma-payer relationships [6]. Indeed, precision medicine is both driving and demanding a shake-up among all stakeholder relationships within healthcare. The biopharma industry's recent embrace of “patient-centricity”—active patient engagement and input at all stages of drug development—is part of the evolution toward precision medicine [7]. As companies seek to develop solutions that expand “beyond the pill” to include, for instance, adherence support technologies or digital tools that engage patients more closely, they are also building treatments that are more targeted to the needs of patients—more precise medicines.
Payers Apply More Precise Targeting to Control Costs
To control costs, government and commercial payers already apply their own criteria to target treatments more carefully to specific segments of their covered populations. Such nonmolecular segmentation has been happening for years in the United States to limit uptake of certain high-priced drugs. Step-therapy regimens start with the most cost-effective drugs, including generics, before offering patients pricier treatments. Prior authorization, demanded by most payers for high-priced drugs, requires doctors to check with insurers, prior to prescribing, whether a particular patient is eligible for reimbursement. In Europe, reimbursement restrictions are imposed by health technology assessment agencies such as the National Institute of Health and Care Excellence in England and Wales, based on cost-effectiveness criteria [8]. (See Chapters 7 and 8.) Patient registries are also used to help identify eligible patients in certain disease areas and reduce off-label therapy use.
More accurate definitions of disease subtypes and the drive toward specialty medicines with correspondingly high price tags reinforce this payer-driven segmentation. So does tighter drug labeling by regulators. The US Food and Drug Administration (FDA) approved Amgen and Sanofi/Regeneron's PCSK9 inhibitors Repatha (evolocumab) and Praluent (alirocumab), not as a primary prevention for all patients with high cholesterol (akin to statins), but for patients whose cholesterol remains high despite taking statins (as well as for patients with certain genetic disorders characterized by high cholesterol) [9]. Critically, the FDA did not specifically approve the drugs for patients who cannot tolerate statins, saving payers millions by providing the justification for restricting the drugs to second-line therapy. Indeed, utilization management requirements enacted by payers successfully narrowed their use despite the collection of outcomes data showing a reduction in the risk of major adverse cardiovascular events. The uptake of both drugs following launch was below expectations [10].
Providers Experiment with Precision Medicine Programs
Providers, too, are under growing pressure to improve outcomes while lowering costs as part of a broader quest to deliver value-based healthcare. In the United States, established integrated delivery networks such as InterMountain Healthcare or the Swedish Medical Center, plus top-ranking academic medical centers like the Mayo Clinic, Columbia University Medical Center, and Stanford University are among the frontrunners in installing the systems and infrastructure necessary to implement genomics-driven precision medicine–at least in oncology. These organizations want to offer their patients the treatments most likely to be effective, but they also want to use the information to reduce inappropriate medication use and unnecessary costs.
For now, the majority of precision medicine programs are limited to the most sophisticated, best-funded health systems and medical research centers and to patients with end-stage cancers. But all providers have at their disposal a burgeoning suite of new technologies and data sources to deliver care that is better tailored to the individual needs and priorities of patients, even if these are not all defined at the genomic level. For instance, hospitals or provider groups that adopt electronic health records (EHRs), establish care pathways, and use clinical decision support software and outcomes tracking can provide physicians with the data and support necessary to deliver more patient-appropriate, personalized, higher-quality care across a wide range of conditions. For example, Kaiser Permanente, the largest nonprofit integrated managed care group in the United States, has an EHR system known as KP HealthConnect, which assembles a detailed history for each patient that physicians can access [13].
Digital Precision Medicine
The scope and depth of non “-omic” patient information will only grow as wearable sensors, remote monitoring tools, online information sources, and social networks provide new kinds of data (both “small” and “big”) and new levels of insight into the conditions and needs of individuals. These data, much of it captured in real-time over long periods, include, for instance, activity and movement tracking.
As the kinds of data being collected expand, care of chronic diseases such as rhematoid arthritis and multiple sclerosis will improve, evolving beyond treatment regimens determined by trial-and-error. Sleep and mood patterns can also be captured. Such data may outline behavioral, social, and environmental influences on health and wellness, as well as uncover treatment modality preferences based on lifestyle and/or personal priorities.
All these data may lead to other, potentially lower-cost and more practicable options for segmenting patients to receive the optimal therapeutic regimen: a “digital” form of precision medicine that may be applicable to a wider spectrum of conditions, including chronic diseases such as diabetes and cardiovascular disease. Indeed, in diabetes, the emergence of personalized diabetes management platforms illustrate one possible future direction, where real-time data are used to calibrate therapy advice. To accelerate the use of digital markers in diabetes and other diseases, the US Food and Drug Administration created a digital health unit to clarify and streamline guidelines for connected devices and medical apps.
Digital precision medicine is not here yet: infrastructure, storage, standards, and analytics need to evolve, and strength and relevance of observed influences on disease outcomes must be tested and measured. In the future, however, it might provide an ideal degree of disease differentiation and definition—more detailed than is currently the case across most therapy areas, yet with less granularity (and complexity) than the genomic profile of an individual patient.
This wealth of patient-centric data comes hand-in-hand with greater involvement by patients in their health and well-being – taking precision medicine beyond treatment toward prevention, and toward what scientists at Stanford Medicine refer to as “precision health” [14] (Figure 4-3).
Figure 4-3 Forces driving precision medicine
Precision Medicine in Practice: Lessons from Cancer
Precision medicine has emerged most prominently in cancer, where the links between genetic changes and disease are relatively strong. Cancer is a complex, multigenic disease, but it is often localized within specific tumors whose genetic signatures can be read and analyzed. Advances in tumor characterization and biomarker identification have helped scientists better understand cancer and to develop better treatments for it, although huge challenges remain. Twenty years ago, the mainstays of cancer treatment were chemotherapy and radiation therapy, which indiscriminately kill both cancerous and healthy cells. Since then, dozens of targeted drugs have emerged, designed to identify and attack specific kinds of cancer cells and/or the systems that allow those cells to grow and thrive. Several newer therapeutics help steer the immune system to recognize and fight cancer. Targeted cancer therapies have become the cornerstone of precision medicine, and they continue to become more tightly focused.
In 1997, the FDA approved the first molecularly targeted cancer drug, Rituxan (rituximab), directed at a protein on the surface of immune cells that is involved in the development of cancer. Other targeted drugs quickly followed, including Herceptin (trastuzumab) in 1998, for breast cancer patients overexpressing the HER2 protein, and Novartis's Gleevec (imatinib) in 2001, transforming the prospects for patients suffering from a rare kind of leukemia by counteracting a defect caused by a particular chromosomal mutation. In 2003, AstraZeneca's Iressa (gefitinib) became the first approved drug to inhibit epidermal growth factor receptor (EGFR), a cell-surface protein that, when mutated or overexpressed, can trigger cancer cell growth.
As many cancers were seen to resist existing treatments, a new generation of targeted therapies has emerged. Sprycel (dasatinib) became an option for lung cancer patients resistant to Gleevec, targeting the same mutated protein but via a different mechanism. AstraZeneca's Tagrisso (osimertinib) was approved in November 2015 for lung cancer patients with a mutation that confers resistance to existing EGFR inhibitors such as Iressa and Roche's Tarceva (erlotinib) [15]. Researchers are testing numerous therapeutics combinations: these include regimens that combine targeted drugs or pair targeted drugs with chemotherapy. The goal is to halt the growth of even the most aggressive cancers by simultaneously attacking them via different mechanisms, so there are fewer opportunities to develop drug resistance.
Though targeted, most of these drugs were not initially approved with a companion diagnostic. With a few exceptions (including Herceptin, for which a HER2 test was approved simultaneously, made by a different company), the companion diagnostics came later.
The Companion Diagnostics Challenge
There are several reasons why few drug-diagnostic pairs have been approved simultaneously. In the early years of targeted medicines, researchers often did not know that mutation profiles of individual patients would significantly affect their chance of responding to a drug. Iressa, for instance, showed highly variable responses among (nonselected) trial patients after its US approval; this led to the drug being withdrawn two years later for lack of efficacy [16].
Even when the variation is understood, integrating diagnostics into drug development is challenging. Developers must understand early on which patients are most likely to benefit from a drug and identify a biomarker that reliably selects those patients. The diagnostic itself must then progress via a separate regulatory path, adding cost and complexity to an already difficult process. Finally, therapies whose use is contingent on a positive test result will have a more limited market than those without such restrictions. In other words, the commercial incentive for biopharmaceutical companies to codevelop diagnostics for their targeted therapies has, to date, been lacking.
This is changing, however. With payers pushing back on high-priced medicines and demanding proof of outcomes, and R & D costs continuing to rise, biopharma firms are starting to use precision medicine to their advantage.
Identifying prospectively the patients most likely to respond to a particular drug can cut development costs by lowering the number of patients required in the clinical trials [17]. It may also shorten the time to approval. (See boxed text, “Tagrisso versus Iressa: From Defensive to Offensive Targeting”) Both factors increase R & D efficiency. Having a test will not guarantee the drug works, even in the selected group, but at least it may reduce the cost of failure.
Targeting May Boost Efficacy but Not Necessarily Sales
In principle, drugs that are used with companion diagnostics may also command a higher price, given a narrowly defined target population and high probability of successful outcomes in that group. In practice, pricing will depend on the competition and other market conditions.
Certainly, targeting does not preclude blockbuster status: Herceptin, which includes a companion diagnostic, had sales of $6.7 billion in 2016 [18]. Companion diagnostics can also revive aging drugs: FDA approval of a companion diagnostic for Tarceva in 2013, for instance, helped make Roche's nine-year-old therapy a first-line therapy for EGFR-positive metastatic non-small cell lung cancer [19].
Wider evidence that more narrowly targeted drugs garner higher sales is only now starting to emerge–and for many is elusive. The battle between Bristol-Myers Squibb's Opdivo (nivolumab) and Merck's Keytruda (pembrolizumab) illustrates the current market dynamics. Approved in 2015 to treat non-small cell lung cancer, both drugs target the programmed-death protein, PD-1. But use of Keytruda, unlike Opdivo, is limited to patients that overexpress the PD-L1 biomarker. As described in greater detail in Chapter 5, Opdivo initially won greater market share; however, subsequent clinical trials in the first-line setting failed to demonstrate Opdivo's superiority to chemotherapy, while Keytruda had a positive result. Keytruda's better first-line data gave Merck's drug an advantage, even with the added complexities associated with administering a companion test. Indeed, the May 2017 FDA approval of Keytruda use based on a specific biomarker, not the location where the tumor originated, is an important step in solidifying Keytruda's advantages over rival therapies. More broadly, it is a critical advance for the field of precision medicine itself [20,21].
The unfolding clinical results for Keytruda and Opdivo illustrate the broader challenges associated with effective development and use of drug-diagnostic combinations. Diagnostics are not routinely administered, including the PD-L1 diagnostic approved concurrently with Keytruda. Many physicians are not aware of the diagnostics that are available, nor are they educated in how to use them. Those who have this knowledge do not always have time to wait for the test to be ordered and for the results to return, which can take several days. Furthermore, diagnostics do not always provide clear, binary (yes or no) information: PD-L1 overexpression, for example, is a sliding scale. It provides a suggestion, not a definitive signal, as to whether the drug will work in a particular patient. Some feel, for example, that PD-L1 expression is not a sufficiently robust selection tool. Given those uncertainties, faced with two comparable drugs, one of which requires extra testing, many physicians may, understandably enough, prefer taking the easier route [22].
Challenges: Scientific, Infrastructural, Regulatory, and Commercial
The conceptual advantages of diagnostic-driven precision medicine are clear: for patients, in avoiding unnecessary treatments; for payers, in avoiding unnecessary costs; for providers, in driving cost-effective outcomes; and for biopharma, in improving R & D economics. But scientific, regulatory, educational, and commercial challenges have hampered the development and widespread adoption of drug-diagnostic pairings [25].
Drug-diagnostic pairs account for a small minority of all cancer drugs. Of the major international biopharma firms, only Roche has embraced diagnostics consistently and wholeheartedly. This was further evidenced by its January 2015 deal to acquire a majority stake in Foundation Medicine, a cancer genomics company.
Genomic sequencing is advancing rapidly, but there are still only a handful, out of many thousands, of genes or gene mutations that can be addressed with current therapies [26]. Additional targeted therapies are making their way through the development process, and the number of biomarker-based tests being ordered is increasing [27].
But the wider use of companion diagnostics requires an educational effort that has so far been lacking. At the most basic level, physicians and patients need to be aware of what tests are available, how they are used, how accurate and reliable they are, and how they can help direct treatment. They also require clarity on what tests are covered by insurance.
Beyond that, physicians must appropriately communicate the benefits and risks of these tests to their patients—whether, for instance, the result can be relied upon to determine treatment choice or indeed the withdrawal of treatment. This can present legal and ethical challenges.
Meanwhile, discovering new, clinically relevant biomarkers is difficult. Recruiting and designing studies of biomarker-targeted medicines, alone or in combination, requires new clinical trial methods and new evidence standards. Adaptive trial designs, whereby trial parameters (dosage, patient selection criteria, drug mix, or other) are altered in response to intermediary results, are gaining traction and have regulatory support. But more expertise and infrastructure is needed for their widespread application [28]. Some argue that studies of a single person—“N of one” trials—will be a critical component of precision medicine, though it is unclear how data from these studies will be used [29]. In short, there is one reason why precision medicine has not advanced as quickly as many believed it would: the regulatory and reimbursement environment for diagnostics needs clarification.
Scientific and Clinical Challenges
Cancer (like many other diseases) is highly complex, heterogeneous, and adaptive. Tumors evolve to resist treatment and often interact with other systems but not necessarily in the same ways in each individual.
This disease complexity makes it difficult to reliably pinpoint predictive biomarkers, even for tightly defined disease subtypes. Increasingly complex drug targets and target combinations will add to the challenge of finding and validating biomarkers that are useful in clinical practice. Furthermore, biomarkers are not always binary (expression/nonexpression); they may offer a sliding scale of expression, similar to what is seen with PD-L1.
To better predict responsiveness across a wider range of therapies or therapeutic areas, additional information beyond the genome may be needed, for instance, protein or metabolite data. Tests themselves can cost several thousands of dollars.
However, this does not concern simply the development of additional tests. Updated evidence standards also are needed to determine when particular tumor biomarkers or genetic read-outs may guide patient management. These are difficult to pin down in a fast-moving field where interpreting complex genetic mutation profiles requires significant expertise. There are calls for a broader dialog to determine evidence standards [30].
New clinical trial designs that can take advantage of biomarker-driven healthcare will be required as well [31]. Basic questions remain to be answered, including how many patients and treatment arms and whether biomarker-negative patients should be included. A key unanswered conceptual question is whether patients will be found for trials, or trials designed for patients.
New tools and scientific insights that will help address these challenges emerge rapidly, but biomarker development, standardization, and trial design challenges will take longer to address.
Infrastructure Challenges
Both electronic health records and clinical decision support tools are required for a pharmacogenomics-based clinical strategy; neither are widespread across most provider networks. In addition, such systems will need to be robust enough to accommodate large volumes of unstructured patient-specific data, presenting data storage, usage, and privacy challenges, topics addressed in greater detail in Chapter 10. An added complexity is the lack of interoperability across health systems and between different stakeholders such as laboratories or imaging centers. As a result, many payers are unable to access data related to precision tests to have a timely impact on treatment decisions.
Overcoming the challenges around data sharing requires cultural and mindset shifts as well as legislative change in some cases. Technology barriers will be addressed by the adoption of emerging technologies such as machine learning and cloud computing, as well as new data analytics approaches (see Chapter 10).
Regulatory Challenges
The separate regulatory and reimbursement pathways for diagnostics versus therapeutics add to the complexity and resources required to bring a drug-diagnostic combination to market. The FDA issued guidance on the development and review of companion diagnostics in 2014, but this fell short of defining precise steps necessary to ensure concurrent drug and diagnostic approval [32]. In addition, FDA standards on clinical relevance for diagnostics lack robustness, even though there is increasing clarity on biomarker-based approaches [33]. Equally problematic, there are no data standards for reviewing the cost-effectiveness of diagnostics, and the processes that do exist lack consistency and transparency. Given the high cost of many of the newer tests, this is a gap that has limited the broader adoption of precision medicine practices.
The expected time frame to overcome regulatory challenges is mid-term (five years). The FDA supports personalized medicine approaches, and the growth in drug-diagnostic submissions will compel change. The emergence of clear health technology assessment (HTA) approaches for diagnostics could take longer, judging by the progress rate of collaborative HTA efforts such as EUnetHTA.
Commercial Challenges
Currently, reimbursement of diagnostics and companion diagnostics is inconsistent across different health systems. Many payers do not reimburse for genetic testing, and some that do restrict it to late-stage cancer patients. Furthermore, drugs and diagnostics may be reviewed by separate teams within payer organizations. There is often a lack of evidence to convince payers (including in Europe) of the savings that may result from the upfront cost of a diagnostic test (up to $5,000). Preventative and screening tests are also poorly reimbursed.
Time constraints have also limited the adoption of companion diagnostics, as clinicians may select an alternative drug with no accompanying diagnostic if therapy is urgent and results from a test do not arrive within an acceptable window.
The difficulty of interpreting complicated test results that don't offer a binary treatment action is a further disincentive. Many specialists and patients are not aware of the tests that are available and how these diagnostics can direct treatment. Uncertainties over test reliability may lead to ethical, and even legal, questions around whether to restrict treatment. Because most diagnostics, to date, have emphasized sensitivity (finding true positives) rather than specificity (correctly identifying true negatives), the tests are biased toward finding a problem rather than giving the “all-clear.” This contributes to concerns over inappropriate overtreatment and wasted money and raises evidence hurdles for diagnostic tests. Those evidence hurdles are further exacerbated because sophisticated “multiplex” tests require experts to translate the results into specific clinical actions. As a result, there is uncertainty about who will pay for such analysis and how it will then be used to guide treatment decisions.
For biopharma companies, weak intellectual property protection for diagnostics means there are real disincentives to paying for diagnostic value. Thus, diagnostic makers have struggled to sign partnerships that can adequately reward them for their development work. These lower valuations have resulted in a cycle, where trials using companion diagnostics are inadequately funded and fail to generate the evidence required for widespread use in the market [34]. Adding to the complexity is the fact that different markets have very different regulations and testing standards for diagnostics. That adds to the cost and complexity of a global drug-diagnostic launch, without providing any obvious positive incentives such as more rapid market uptake.
As evidence of cost savings to health systems mount and biopharmas see greater market share for drugs developed in combination with diagnostics, a positive feedback loop will promote their usage and investment in future therapeutic-diagnostic pairings.
The cultural and educational changes required for greater companion diagnostic usage may take longer. It is important to remember that precision medicine is a new discipline that requires new tools and analytics capabilities. In markets where the approach to new treatments is more conservative, access to targeted therapies and the underlying tests associated with their use may be limited.
Digitally driven precision medicine will face a similar set of hurdles. Regulation of mobile phone apps, monitoring devices, and tools is still nascent. So is our understanding of how to best analyze and interpret the massive amounts of new data emerging from digital health technologies, not all of which will be relevant to patient care. New technologies must be further tested for reliability and usability and consumers need and must be able to use wearables, sensors, or apps so that they inform and improve treatment. Reimbursement for these digital medicine tools is an unknown.
Surmounting the Hurdles to Revolutionize Medicine
The challenges facing precision medicine are surmountable. Many are already being addressed, and initial successes will provide the evidence required to overcome the rest. Elucidating the science, establishing standards, and facilitating regulation and reimbursement will all help promote uptake and culture change.
Diagnostic tests are becoming less invasive, more accurate, and cheaper, in part due to technology improvements and growing demand. Efforts are being made to make testing more widely practicable and affordable, for instance, by developing multiplex testing kits that can detect, with one test sample, several of the most common gene mutations across certain cancers [36]. This is particularly attractive to payers who would prefer to pay for a single test to tell which of a multitude of drugs approved for similar indications is most likely to work best, rather than paying for several single-drug-linked tests. These consolidated tumor profile tests are underpinned by high-volume, fast next-generation sequencing (NGS) techniques.
Hurdles remain over interpreting the data from such tests, whether such tests are sufficiently reliable at the specific gene mutation level, and, thus, under what circumstances they may be reimbursed. But assuming the tests eventually offer sufficient accuracy and reliability across the targeted mutations, they may emerge as the most practicable real-world solution in selected therapy areas. In November 2015, Thermo Fisher Scientific signed a deal with Novartis and Pfizer to develop a universal, multi-marker NGS test for use across multiple non-small cell lung cancer drug programs, with the goal of allowing tailored treatment approaches. [37] Foundation Medicine is doing something similar.
Meanwhile, more sophisticated testing options are emerging, covering several other layers of information from within the proteome, transcriptome or microbiome (see text box, “Multi-‘omics’ Analysis”).
New trial designs are being tested. The Lung Cancer Master Protocol (Lung-MAP) trial, for example, is testing four drugs at once, seeking to match biomarkers in lung cancer tumors with a particular mix of these medicines. The idea is to improve and accelerate drug development; certainly, the wider use of biomarkers should make trials more efficient [40].
Regulators, including the FDA, are encouraging biomarker-driven approaches to enable precision medicine. The FDA's Biomarker Qualification Program guides drug developers in their development of biomarkers, helping them integrate these into regulatory reviews, ensuring reliability and validity, and explicitly seeking to “foster biomarker development” [41].
PrecisionFDA is an R & D portal that allows the scientific community to test and validate ways of processing the large amounts of genomic data collected using next-generation sequencing technology. A beta version was launched in November 2015 [42]. The Biomarkers Consortium, a public-private partnership managed by the Foundation for the National Institutes of Health (NIH), is trying to accelerate the development and regulatory approval of biomarker-based technologies and medicines. The European Medicines Agency (EMA) provides advice and opinions on biomarkers and other new approaches but has not issued specific guidance. It is, however, increasing its collaborations with academia to build expertise in the qualification of biomarkers and other new methodologies.
The FDA is also consulting on how digital health technologies and wearables should be regulated, as the potential of these tools in allowing more appropriate, personalized care becomes clearer. Many of these digital tools are being tested in clinical trials, where the FDA has been “incredibly encouraging,” according to Mike Capone, chief operating office at Medidata, which offers cloud-based clinical trial solutions and data analytics [43]. The EMA has not outlined explicitly how it is approaching the digitization of trials and medicines. The organization's 2016 work plan nods to the explosion of new data and tools in healthcare and acknowledges the need for a “robust, agile IT infrastructure” and “new capabilities to manage data” [44].
Building the Precision Medicine Infrastructure
Precision medicine is underpinned by accessible, interpretable data. Millions of data points must be collected systematically and consistently across large cohorts of patients in order to help scientists and clinicians piece together the links between genetics and disease, identify new biomarkers, design targeted therapy trials, and uncover the broader determinants of health outcomes. Collecting and interpreting such data demands multi-stakeholder partnerships. “Rich and highly credible information will be the primary catalyst for the broad adoption of precision medicine, particularly in cancer,” pointed out Michael Pellini, CEO of the genomics company Foundation Medicine, in announcing a 2015 collaboration with global information and technology services group IMS Health [45].
Multiple further data collection efforts are underway, including government-sponsored initiatives, to help drive and inform the science behind precision medicine (Figure 4-4). These efforts require robust, integrated information technology (IT) systems, including data analytics software and expertise. They also demand secure storage of the ever-larger volumes of genomic and related personalized data.
Figure 4-4 Selected precision medicine data collection projects
The digital and data revolution (discussed in Chapter 10) is providing the infrastructure necessary to accommodate precision medicine. New systems, software, and tools have emerged, both from established technology giants such as IBM, Oracle, Apple, Intel, and Alphabet, as well as from start-ups. Several health systems are already using venture-backed Syapse's precision medicine platform, with its clinical and molecular data integration, decision support, outcomes tracking, and a shared-learning loop that allows best practice to evolve based on real-world results. Oracle launched its own Healthcare Precision Medicine software suite in January 2016 [46].
While data collection partnerships accelerate, some of the regulations and guidelines required to manage and control the use of such data are emerging. US data privacy standards are embedded within the HIPAA (Health Insurance Portability and Accountability Act) privacy rule, which protects patient medical records and other health information and limits how this data can be used. The Genetic Information Nondiscrimination Act also helps protect individuals against misuse of their data. But the issue of data privacy and protection remains challenging, especially in some European markets such as Germany.
Meanwhile, the Clinical Pharmacogenetics Implementation Consortium (CPIC) has created guidelines on how to responsibly use genomic data to inform prescribing. The Institute of Medicine's Roundtable on Translating Genomic-Based Research for Health, established in 2007, holds workshops, discussions, and symposia among experts from all stakeholder groups on how to turn genomics into healthcare applications [47].
Payers Begin to Fund Precision Medicine
Payers such as UnitedHealthcare are beginning to cover molecular profiling for certain patient groups, such as stage IV non-small cell lung cancer patients. Independence Blue Cross announced it would cover whole genome sequencing for patients with limited treatment options: those with rare cancers, triple negative breast cancer, and those with metastatic disease who are not responding to other treatments, as well as children with tumors [48]. Several European payers, including the French government, have begun to fund molecular testing in cancer patients [49]. The advantage of Europe's single-payer health systems is that many have the infrastructure necessary to implement diagnostic-driven strategies.
Meanwhile, more targeted products continue to reach the market. Today, only 15 percent of oncology drugs are considered targeted; earlier in the pipeline, this figure is 50 percent [50]. As these therapies become available and as disease characteristics and subtypes continue to be better understood, the hurdles facing precision medicine will continue to fall.
Biopharma Must Drive, not be Driven by, Precision Medicine
As hurdles are overcome, the forces driving precision medicine will be unstoppable. Payer-driven patient segmentation will continue as costs rise. The digitization of medicine will inevitably increase, along with the shift to outcomes. This leaves biopharma companies with no choice but to embrace precision medicine, in all its forms. The biopharma industry must develop medicines that are sufficiently targeted to drive consistent, reimbursable outcomes, rather than risk having payers restrict them according to their own criteria.
More widespread use of diagnostics may limit the number of patients treated with any particular drug. Yet a smaller, better-defined market may be a worthwhile trade-off for gaining differentiation in competitive markets and to optimize outcomes. Drugs that are effective in broad populations are relatively rare. It may be premature to definitively correlate molecularly targeted treatments with better sales, but prescribing treatments to nonresponders is no longer tenable in a value-based care system, either financially (for payers) or from a reputational standpoint (for biopharma).
Precision medicine ultimately should lead to greater commercial success by offering increased R & D efficiency (because trials will be smaller and less expensive and development times will be shorter), more focused regulatory submissions, and more secure reimbursement and uptake. Precision medicine also demands and encourages greater stakeholder collaboration—including with patients—in a healthcare landscape that calls increasingly for combined expertise and data sharing.
Precision Medicine Demands New Kinds of Collaboration
Implementing precision medicine requires new kinds of partnerships and collaboration, both among stakeholders in healthcare but also with experts across technology, data analytics, and beyond (Figure 4-5). Drug developers are already pairing up with each other to test drug combinations, such as targeted cancer therapies with immuno-oncology approaches. They also need to partner with diagnostics firms that have market-relevant regulatory and commercial expertise. Such partnerships have increased, though not consistently (Figure 4-6).
Figure 4-5 Precision medicine requires biopharma's engagement in multiple stakeholder alliances
Figure 4-6 Evolution of deal making between drug and diagnostic developers
Incentives between biopharmaceutical and diagnostics firms are not always aligned. Drug developers want an accurate, low-cost test to be available fast in order to open up the relevant patient population for a drug, which could bring in significant revenues, especially if used chronically. Diagnostics developers are only paid when the patient is tested. If the population is too small, the reimbursement math might not work. For larger populations, marketing and education on how to access and use the test may arise, and competing diagnostics are likely to arrive fast, pushing down prices.
Solutions to these divergent incentives include buying diagnostics capabilities, as Roche has done, or signing longer-term deals with large, multi-platform diagnostics firms. Eli Lilly and Janssen both did this in 2014, signing broad-based deals with Qiagen and Adaptive Biotechnologies, respectively [51,52]. Consolidation among diagnostics groups makes this option viable, in terms of accessing the necessary range of capabilities, but also potentially more expensive, as the number of potential partners is reduced.
A Framework for Biopharma-Payer Risk Sharing
Precision medicine also requires biopharma companies to build more collaborative relationships with payers, and it provides a framework for doing so by encouraging outcomes-linked payment deals.
Payers want to see evidence that diagnostic-linked therapies, or digital medicine, are practical and provide value for money before they start paying for tests (which can cost from hundreds to several thousands of dollars) as well as the treatments. One answer is payer-biopharma risk sharing: deals that link drug pricing (and/or reimbursement level) to the real-world outcomes that a treatment delivers. These deals, described in more detail in Chapter 7, were shunned by both sides until recently, but are beginning to gain traction as the broader shift to value-based care takes hold. Tightly defined patient groups and high response rates, as seen with precision medicines, should give both biopharma companies and payers more confidence in tying price to outcomes.
The digitization of healthcare and the concurrent rise of the connected, empowered consumer are already forcing patient-centric approaches among several biopharma firms [53]. They are also driving some of the interdisciplinary collaboration and experimentation required for precision medicine. New technologies, apps, wearables, and burgeoning sources of genomic and non-genomic big data are converging to transform how drug trials are designed and recruited and how therapies are administered, and they are expanding the definition of therapies “beyond the pill.” Experiments abound across beyond-the-pill solutions, mobile health, adaptive regulatory pathways and trial designs, and include greater patient engagement, wider use of patient-reported outcomes (PROs) and electronic PROs, and more. These experiments help define and set the rules for a future of more targeted medicines.
Biopharma and diagnostics firms must maintain frequent dialog with regulators, as the latter adjust their processes to accommodate biomarker-based drug development, new kinds of data, and increasingly digital medicines. The priorities of drug regulators are safety and efficacy, but they also, like biopharma, want more efficient drug development and faster access to effective medicines.
Precision Medicine's Future
Molecular-driven precision medicine is still in its infancy. It emerged out of cancer drug discovery and it is there, more specifically in late-stage cancers, that it has taken root. As evidence of its success begins to emerge, in terms of both outcomes and costs, it will spread across other therapy areas. The FDA approved 13 novel personalized medicines in 2015, according to the Personalized Medicine Coalition. Eight were in conditions other than cancer, including asthma, schizophrenia, cystic fibrosis, and high cholesterol.
But the spread of diagnostic-driven personalized medicine will not happen overnight. “Changing medicine…from over-use and over-treatment to a more accurate precision medicine is a 20-year process,” opines Tom Miller, cofounder of GreyBird Ventures, which invests in precision-medicine-focused start-ups [54]. Nor is molecular-based precision medicine likely to be possible, practicable, and affordable across all conditions. For many chronic diseases, “digital” precision medicine, that is, more targeted treatment (and prevention) approaches enabled by new wearable tools and data sources, may prove more fruitful and cost effective. “It's about finding a level of data that defines and differentiates disease in a much more specific way than we do today,” sums up Mara Aspinall [55].
Precision medicine will personalize rather than replace traditional, population-based care. The drive toward more specialist, targeted medicines cannot continue ad infinitum. Health systems will not be able to afford the rising prices that would accompany products addressed at ever-narrower conditions. Precision medicine must be applied pragmatically across therapy areas, health systems, and geographies to enable more intelligent population-based approaches to treating some chronic diseases, as well as diagnostic-driven, targeted therapies at the individual level in other conditions.
Precision medicine may also drive more targeted prevention strategies, in theory helping to control costs. Molecular markers can signal disease risk before symptoms appear, allowing screening efforts to be focused on those at risk. Women with certain gene variations (BRCA1 or BRCA2), for instance, are more than six times more likely to develop breast cancer during their lifetime than those without. Grail, a new spin-off from gene sequencing giant Illumina, is developing a blood test (known as a “liquid biopsy”) that can detect tiny fragments of cancer DNA well before any tumor is detected [56]. Others have similar programs underway. Any resulting test would have to be highly accurate, however, and any therapy given to apparently healthy individuals would need to be highly targeted indeed, with no or minimal side effects.
Biopharmaceutical firms must ensure their portfolio includes candidates across disease segments that are sufficiently well defined to allow targeted therapeutic approaches, yet big enough to sustain a business. Companion diagnostic strategies must be planned in advance, with sufficient flexibility to respond to market moves toward multiplex testing and evolving attitudes of payers. Those companies addressing large chronic diseases must access the relevant technologies and expertise to enable digital precision medicine and adapt product pricing, positioning, and marketing tactics rapidly as real-world, often real-time, data emerge on how and by whom medicines are used, and with what outcomes.
Precision medicine is not emerging in isolation but rather alongside other, equally disruptive, changes across healthcare. By adopting flexible, partnership-driven approaches, remaining focused on how to achieve the best outcomes for patients, and opening up to new kinds of expertise, biopharmaceutical firms can emerge stronger, more efficient, and more engaged with their customers.
Summary Points
- Precision medicine—getting the right treatment to the right patient at the right time—is growing fast, driven by technological advances (e.g., genomics and sequencing technology) and market forces (e.g., patient empowerment and the need to reduce the costs of inappropriate therapy).
- It is particularly prominent in cancer treatment, where targeted therapies and drug-device combinations are multiplying, offering new options for patients.
- Precision medicine is not only about drug-device pairs; it encompasses a wider range of tools and mechanisms to narrow treatments more precisely to the needs of individual patients and thereby achieve better outcomes. These new tools include a host of digital technologies, such as wearable sensors and smartphone apps that provide patient insights and offer more tailored treatment modalities.
- In the future, these newer digital technologies will enable P-medicine (personalized, precision, preventative, predictive, pharmacotherapeutic, and patient participatory medicine).
- The rise of precision medicine has profound implications for biopharmaceutical firms. It is changing the kinds of medicines they develop, how they develop them, and how they are reimbursed and marketed.
- The commercial model for molecular-based precision medicine is not yet clear. Narrower targeting means smaller patient populations. For now, there is not much evidence correlating more targeted treatments with higher sales. Payers cannot support ever-higher prices, yet prescribing treatments to nonresponders is no longer tenable in an increasingly value-based system.
- Ultimately, precision medicines should enable greater R & D efficiency, via smaller, more targeted trials, and allow more focused regulatory submission and more secure reimbursement and uptake.
- There are scientific, structural, regulatory, commercial, and educational challenges facing the spread of precision medicine. Collecting and interpreting huge volumes of data, and applying it to clinical decision-making, requires new expertise, systems, and processes. Meanwhile, regulations around companion diagnostics are in flux, reimbursement is inconsistent, and uptake is limited.
- These challenges are being addressed. Major data collection projects are underway; regulators are encouraging biomarker-based strategies; and payers are beginning to fund some kinds of molecular profiling.
- The rise of precision medicine requires biopharmaceutical firms to build new kinds of partnerships with diagnostics players, technology companies, and payers. Many such deals are already in place.
- Precision medicine also provides a framework for more robust, data-backed, outcomes-based deals with payers. Furthermore, it embodies and enables the patient-centric approach to medicine that biopharma says it strives to deliver.